A Genetic Decomposition Algorithm for Predicting Rainfall within Financial Weather Derivatives
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gp-bibliography.bib Revision:1.8081
- @InProceedings{Cramer:2016:GECCO,
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author = "Sam Cramer and Michael Kampouridis and Alex Freitas",
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title = "A Genetic Decomposition Algorithm for Predicting
Rainfall within Financial Weather Derivatives",
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booktitle = "GECCO '16: Proceedings of the 2016 Annual Conference
on Genetic and Evolutionary Computation",
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year = "2016",
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editor = "Tobias Friedrich and Frank Neumann and
Andrew M. Sutton and Martin Middendorf and Xiaodong Li and
Emma Hart and Mengjie Zhang and Youhei Akimoto and
Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and
Daniele Loiacono and Julian Togelius and
Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and
Faustino Gomez and Carlos M. Fonseca and
Heike Trautmann and Alberto Moraglio and William F. Punch and
Krzysztof Krawiec and Zdenek Vasicek and
Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and
Boris Naujoks and Enrique Alba and Gabriela Ochoa and
Simon Poulding and Dirk Sudholt and Timo Koetzing",
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pages = "885--892",
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keywords = "genetic algorithms, genetic programming",
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month = "20-24 " # jul,
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organisation = "SIGEVO",
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address = "Denver, USA",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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isbn13 = "978-1-4503-4206-3",
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DOI = "doi:10.1145/2908812.2908894",
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abstract = "Regression problems provide some of the most
challenging research opportunities, where the
predictions of such domains are critical to a specific
application. Problem domains that exhibit large
variability and are of chaotic nature are the most
challenging to predict. Rainfall being a prime example,
as it exhibits very unique characteristics that do not
exist in other time series data. Moreover, rainfall is
essential for applications that surround financial
securities such as rainfall derivatives. This paper is
interested in creating a new methodology for increasing
the predictive accuracy of rainfall within the problem
domain of rainfall derivatives. Currently, the process
of predicting rainfall within rainfall derivatives is
dominated by statistical models, namely Markov-chain
extended with rainfall prediction (MCRP). In this
paper, we propose a novel algorithm for decomposing
rainfall, which is a hybrid Genetic Programming/Genetic
Algorithm (GP/GA) algorithm. Hence, the overall problem
becomes easier to solve. We compare the performance of
our hybrid GP/GA, against MCRP, Radial Basis Function
and GP without decomposition. We aim to show the
effectiveness that a decomposition algorithm can have
on the problem domain. Results show that in general
decomposition has a very positive effect by
statistically outperforming GP without decomposition
and MCRP.",
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notes = "GECCO-2016 A Recombination of the 25th International
Conference on Genetic Algorithms (ICGA-2016) and the
21st Annual Genetic Programming Conference (GP-2016)",
- }
Genetic Programming entries for
Sam Cramer
Michael Kampouridis
Alex Alves Freitas
Citations